8 results about "Random search algorithm" patented technology

The invention discloses a large hydroelectric generator confluence copper ring arrangement optimization method based on a random search algorithm and a greedy strategy. The method comprises the following steps: step 1, establishing a model of a confluence copper ring arrangement scheme; 2, establishing a spatial information model of the confluence copper ring; step 3, constructing constraint conditions of the confluence copper ring; step 4, establishing an optimization objective function; and 5, optimizing a confluence copper ring arrangement scheme by using a random search algorithm in combination with a greedy strategy. The method effectively solves the problems of low manual arrangement efficiency, serious material waste and the like in a traditional copper ring wiring mode, remarkably improves the copper ring confluence efficiency, and is particularly suitable for large hydroelectric generators with huge structures, complex channels and numerous parallel branches.

The application discloses a storm surge artificial intelligence prediction method based on physical equation constraints and relates to the technical field of artificial intelligence storm surge prediction, and comprises the following steps: step 1, collecting multi-source data; step 2, data assimilation and preprocessing; step 3, realizing a machine learning calculation method of physical loss; step 4, constructing an artificial intelligence model jointly constrained by physics and data; step 5, using a random search algorithm to adjust hyperparameters, and further optimizing the model; and step 6, performing explainability analysis on a prediction process and results. The three control equations used for storm surge calculation are introduced into the machine learning model, the dependence on the training data volume is greatly reduced compared with a traditional data-driven intelligent prediction model, the trend of the change of the physical variables in the prediction process is analyzed, visual display and uncertainty analysis are performed, and the unexplainability problem of the artificial intelligence prediction model is solved.

The invention discloses a method and a system for realizing multispectral switching by cooperatively regulating and controlling pumping and polarization states, and belongs to the technical field of ultrafast fiber lasers and automatic control. The method comprises the following steps: inputting an output signal of an ultrafast fiber laser into a residual neural network for identification and obtaining an ensemble solution of voltage and pumping power corresponding to a spectrum; searching optimal set solutions corresponding to different spectrum states by using a self-adaptive gradient optimization algorithm; a search result is fed back to an electric control polarization controller and a pump, so that automatic switching of spectrum states is realized; if it is monitored that the spectrum state disappears, an adaptive recovery algorithm is executed, and random extraction is carried out in the set solution for recovery; and if recovery fails, searching through a random search algorithm until recovery. The problem that a passive mode-locking fiber laser based on nonlinear polarization rotation cannot rapidly switch the pulse state and is difficult to stabilize is solved, meanwhile, full-automatic control over the fiber laser is achieved, the universality of an algorithm is improved, and the structure is simple and easy to achieve.

The invention relates to the technical field of protection engineering, in particular to a facility system first-aid repair resource allocation method for a complex uncertain attack scene, which comprises the following steps of: S1, acquiring a facility system network structure and a facility number, and evaluating a facility initial function; s2, determining the level of an adverse event; s3, determining facility system first-aid repair resource preset planning constraint conditions, and constructing an improved facility system first-aid repair resource preset planning model; and S4, solving the first-aid repair resource preset planning model by adopting an adaptive random search algorithm (ARS-SVM) fused with a support vector machine to obtain an optimal solution of the first-aid repair resource configuration of the facility system. According to the method, the resource demand satisfaction degree expectation which maximally considers the conditional value-at-risk is taken as a target, the demand is regarded as a random variable, the probability distribution is obtained through historical data fitting, and the auxiliary variable is introduced to quantify the demand fluctuation, so that the urgent repair recovery accuracy can be improved, the cost effectiveness can be balanced, and the system adaptability can be enhanced.

Various embodiments relate to a method of testing a plurality of devices of the same type wherein each of the plurality of devices of the same type include a built-in self-test device, including: randomly generating, by a processor, stimulus parameters; applying, by the built-in self-test devices, the generated stimulus parameters N times to the plurality of devices of the same type; measuring, by the plurality of devices of the same type, a response of the plurality of devices of the same type to the generated stimulus parameters to produce M×N response outputs, where M is a number of the plurality of devices of the same type; calculating, by the processor, a defect likelihood for a test set of the plurality of identical devices based upon a mean of a reference set of the plurality of identical devices response outputs, a mean of the test set response outputs, a standard deviation of reference set response outputs, and a standard deviation of the test set response outputs; determining, by the processor, that the defect likelihood for the test set is greater than a first threshold value; applying, by the processor, an initial step of a directed random search algorithm to update stimulus parameters in response to determining that the defect likelihood is greater than the first threshold; applying, by the built-in self-test devices, the updated stimulus parameters N times to the plurality of devices of the same type; measuring, by the plurality of devices of the same type, a response of the plurality of devices of the same type to the updated stimulus parameters to produce M×N updated response outputs; calculating, by the processor, a defect likelihood for the test set based upon a mean of the reference set updated response outputs, a mean of the test set updated response outputs, a standard deviation of reference set updated response outputs, and a standard deviation of the test set updated response outputs; and determining, by the processor, that the defect likelihood for the test set is greater than a second threshold, wherein the second threshold is greater than the first threshold.

The invention discloses a multi-lake model automatic calibration method based on a random search algorithm. The method comprises the following steps: S1, data preparation and operation environment initialization; s2, parameter space construction and search control setting; s3, candidate parameter combination generation; s4, parameter writing and multi-model operation; s5, output analysis and time sequence alignment are carried out; s6, performance evaluation and effectiveness judgment; s7, performing global random search and optimal candidate screening; s8, local refined searching and optimal parameter updating are carried out; s9, summarizing results and determining final parameters; according to the method, unified initialization, parameter writing, synchronous operation and analysis and evaluation record output are performed on four lake models including FLake, GLM, GOTM and Simstrap under the same framework, manual configuration and repeated trial and error are remarkably reduced, and calibration efficiency and reproducibility are improved.

The application discloses a pilot optimization method based on a combination of a genetic algorithm and a random search algorithm for an OTFS system, and the method comprises the following steps: initializing a population; calculating the fitness value of each individual in the population; entering a breeding cycle iteration; performing a cross-recombination operation on offspring; performing a mutation operation on the offspring; performing a reinsertion operation on the offspring; selecting excellent individuals and performing a random search algorithm on the excellent individuals; aiming at a pilot optimization problem in channel estimation of an orthogonal time frequency space (OTFS) system based on compressed sensing, taking minimization of a cross-correlation value of a recovery matrix as a target, and taking the minimum cross-correlation value as a criterion for selecting the best pilot. The method has the advantages of the genetic algorithm, and a part of the population is randomly searched to avoid falling into a local optimum problem. Compared with a conventional genetic algorithm, the method has better effect, and a pilot obtained by using the method can obtain a lower channel estimation mean square error when applied to channel estimation.

The embodiment of the invention discloses a parameter optimization-based wind power plant single-machine aggregation method and device, equipment and a medium, and the method comprises the steps: carrying out the single-machine equivalence of a target wind turbine generator cluster in a wind power plant, and obtaining an equivalent unit of the target wind turbine generator cluster; performing aggregation according to all the equivalent units to obtain a doubly-fed fan simulation model of the wind power plant; searching the control parameters of the doubly-fed fan simulation model based on a grid random search algorithm to obtain the control parameters of the doubly-fed fan simulation model; and configuring a doubly-fed fan simulation model according to the control parameters so as to realize aggregation of equivalent units. The method comprises the following steps: performing single-machine equivalence on each wind turbine generator cluster to obtain a corresponding equivalent unit, aggregating the equivalent units to obtain a doubly-fed fan simulation model, and obtaining an optimal solution of control parameters of the doubly-fed fan simulation model through a grid random search algorithm to achieve the best fitting effect of the doubly-fed fan simulation model. The simulation model has the advantage of being good in simulation effect.